diff options
author | David Luevano Alvarado <55825613+luevano@users.noreply.github.com> | 2020-02-29 11:31:40 -0700 |
---|---|---|
committer | David Luevano Alvarado <55825613+luevano@users.noreply.github.com> | 2020-02-29 11:31:40 -0700 |
commit | 9e90b73721edf370f7be9c77aba8431bbe762bdb (patch) | |
tree | 899c9e625e2a4e90c76a99f032e98fae725c80cf | |
parent | 1384a40fb90f9118410d5473634ddbff8b628841 (diff) |
float to np float)
-rw-r--r-- | ml_exp/kernels.py | 5 |
1 files changed, 2 insertions, 3 deletions
diff --git a/ml_exp/kernels.py b/ml_exp/kernels.py index e6855f97e..feaf9a990 100644 --- a/ml_exp/kernels.py +++ b/ml_exp/kernels.py @@ -37,9 +37,9 @@ def gaussian_kernel(X1, """ i_sigma = -0.5 / (sigma*sigma) - K = np.zeros((X1.shape[0], X2.shape[0]), dtype=float) + K = np.zeros((X1.shape[0], X2.shape[0]), dtype=np.float64) if opt: - # Faster way of calculating the kernel. + # Faster way of calculating the kernel (no numba support). for i, x1 in enumerate(X1): if X2.ndim == 3: norm = np.linalg.norm(X2 - x1, axis=(1, 2)) @@ -50,7 +50,6 @@ def gaussian_kernel(X1, for i, x1 in enumerate(X1): for j, x2 in enumerate(X2): f_norm = np.linalg.norm(x2 - x1) - # print(f_norm) K[i, j] = math.exp(i_sigma * f_norm**2) return K |